adaptive intelligent tutoring system for learning computer

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ISSN 2286-4822

www.euacademic.org

EUROPEAN ACADEMIC RESEARCH

Vol. IV, Issue 10/ January 2017

Impact Factor: 3.4546 (UIF)

DRJI Value: 5.9 (B+)

Adaptive Intelligent Tutoring System for Learning

Computer Theory

MOHAMMED A. M. AL-NAKHAL

Department of Information Technology

Faculty of Engineering & Information Technology

Al-Azhar University, Gaza, Palestine

SAMY S. ABU NASER1

Professor of Artificial Intelligence

Department of Information Technology

Faculty of Engineering & Information Technology

Al-Azhar University, Gaza, Palestine

Abstract:

In this paper, we present an intelligent tutoring system

developed to help students in learning Computer Theory. The

Intelligent tutoring system was built using ITSB authoring tool. The

system helps students to learn finite automata, pushdown automata,

Turing machines and examines the relationship between these

automata and formal languages, deterministic and nondeterministic

machines, regular expressions, context free grammars, undecidability,

and complexity. During the process the intelligent tutoring system

gives assistance and feedback of many types in an intelligent manner

according to the behavior of the student. An evaluation of the

intelligent tutoring system has revealed reasonably acceptable results

in terms of its usability and learning abilities are concerned.

Key words: Intelligent Tutoring System, Authoring Tool, ITSB,

computer theory

1 Corresponding Author: [email protected]

Mohammed A. M. Al-Nakhal, Samy S. Abu Naser- Adaptive Intelligent Tutoring

System for Learning Computer Theory

EUROPEAN ACADEMIC RESEARCH - Vol. IV, Issue 10 / January 2017

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INTRODUCTION

In mathematics and computer science, computer theory course

is the branch that involved with how efficient a problem may be

solved using algorithmic computational model. The field

consists of three main divisions: theory of computability,

language and automata theory, theory of computational

complexity that is associated with the question: "What are the

essential capabilities and restrictions of computers?" To be able

to implement a rigorous study of computation, computer

experts who work with a computers mathematical abstraction

named computational model. There are a few models that are

being used, but the most frequently studied is the Turing

machine. Computer experts study the Turing machine due to

its simplicity in formulation, may be examined and used to

verify results and since it denotes what numerous experts

debate the most influential expected model of computation). It

may appear that the hypothetically infinite memory is an

unattainable attribute, but a decidable problem resolved using

Turing machine will need solely finite a quantity of memory.

Thus, in theory, any problem that can be resolved by a Turing

machine can be resolved by a computer that incorporates finite

a quantity of memory [2,3].

The basic theory of learning styles is that different

people learn in a different ways. One way to see the learning

styles is to connect them with the learning cycle advocated by

Kolb, where learning is seen as a continuous process based on

practical experience that incorporates a set of observations and

reflections [6].

Later, Mumford Honey developed this model by creating

a questionnaire for the styles of learning using the model

suggested by Kolb. Authors identified four learning styles,

related to the different four stages of the learning cycle which

was suggested by Kolb. They are theorist, activist, pragmatist,

and reflector.




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These styles became very important in education, given the

change in the educational prototype presented by the changes

to the society of knowledge. The ultimate learning prototype

introduced new learning settings, which are progressively more

heterogeneous, where is significant to be taken into

consideration the learning styles of every student to offer an

education more actual and concentrated on the student [16,18].

Figueiredo and Afonso considered the setting and

content as the main parts of the learning model. The learning

model identified the learning activities as the sittings in which

people learn. The content is the data that is organized and

contains: text, resources, multimedia and lessons. The setting

is a group of surroundings that are appropriate to the user to

construct knowledge by its linking to the content [18].

In this model, the instructor has a mutual role in the

arrangement of content and generating the learning setting.

The setting can be a lecture hall or a virtual learning site, in

which the role of instructor is more concentrated on content in

the case of a lecture hall, and the setting in the case of a virtual

learning site.

Contents undertake the role of broadcast the knowledge,

where data is converted into knowledge by a known learning

activity [14,24].

The incorporation of intelligent systems in the learning

manner support permits an adaptation of content and settings

into the learning style of every student, making adaptive tools

to support cooperation [24]

The intelligent tutoring system for learning computer

theory was designed and developed using ITSB authoring tool

[1].

LITERATURE REVIEW

There are many intelligent tutoring systems designed and

developed for the education purposes. Some of these ITS




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dedicated to teaching computer science like [11, 12, 13, 15],

English language such as [19] and Mathematics such as [22].

Teaching Java objects Programming language[5], ITS for

helping Computer Science students to learn debugging skills

[11], ITS helping Computer Science students to learn

Computer Programming [8, 23], an Intelligent Tutoring

System for Teaching Grammar English Tenses[9], ITS for

helping students who are 8-to12-year-olds to learn Primary

Mathematics[22], A comparative study between Animated

Intelligent Tutoring Systems (AITS) and Video-based

Intelligent Tutoring Systems (VITS) [4], An agent based ITS for

Parameter Passing In Java Programming[12], Java Expression

Evaluation [18], Linear Programming[13,17], Design and

Development of Diabetes Intelligent Tutoring System [16],

effectiveness of e-learning[7], computer aided instruction[21],

effectiveness of the CPP-Tutor[10], teaching AI searching

algorithms[24], teaching database to sophomore students [15],

Predicting learners performance using NT and ITS [20], and

intelligent tutoring system for teaching advanced topics in

information security[26], development and evaluation of the

Oracle Intelligent Tutoring System (OITS)[27].

ITS ARCHITECTURE

Figure 1: Computer Theory ITS Architecture

A general intelligent tutoring system architectures as in Figure

1 consists of the student model, interface model, domain model,

and pedagogical model [8].

In the computer theory intelligent tutoring system, the

student plays the main role. All the characteristics of the




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system have the task to adapt the pedagogical, material, the

interface to the student profile and his preferring.

The domain model is the knowledge management

system that stores the material which will be presented to the

student.

The student model signifies learning style, the student

behavior, motivation level, his profile, and his preferences. This

model is grounded on artificial intelligence skills that emulate

the human behavior. All the student behavior is stored in the

system and used for reasoning and adapting the domain model

to the student needs.

The pedagogical model acts as virtual instructor,

exhibiting the materials in a suitable sequence, according to

learning style and student skills. This process is an interactive

and this model job is to illustrate the concepts to the student

and supporting all the learning process [9].

The interface model has a very significant job. When an

ITS have strong pedagogical, domain, and student models, but

the interface model is very bad, the ITS will not be useful

because the interface is the door of all the system and has the

capability to lid all the attention of the student. To develop a

good interface model, it is necessary to take into consideration

the usability matters of a user computer interface, since this

model communicates with the user and the other models of the

system [1, 8].

DOMAIN MODEL ARCHITECTURE

The topics covered in this Intelligent Tutoring system as follows

[2,3]:

Basic concepts and definitions; Set operations; partition

of a set

Equivalence relations; Properties on relation on set.

Proving Equivalences about Sets.

Central concepts of Automata Theory.




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Regular Expressions; Operations on Regular expressions

Finite Automata and Regular Expressions.

Conversion from FA and regular expressions.

Deterministic Finite Automata (DFA); Minimization of

DFA.

Non-Deterministic Finite Automata (NDFA).

Equivalence of Deterministic and Non-Deterministic

Finite Automata.

Equivalence between DFA,NFA, NFA-Λ

Context-Free Grammars.

Parse Trees; Ambiguity in Grammars and Languages.

Standard Forms; Chomsky Normal Forms;

Minimization of CFG’s.

Pushdown Automata (PDA).

Deterministic and Non-Deterministic (PDA); Formal

definition of NPDA.

Transition functions of NPDA; NPDA Execution.

Accepting Strings with NPDA; Equivalence of PDAs and

CFG.

The Turing Machine.

Programming Techniques for Turing Machines; Formal

definition of TM’s.

Complexity issues and analysis

STUDENT MODEL ARCHITECTURE

The intelligent tutoring system logs the student data in a

database. It stores the basic data of the students such his/her

name, user id, current score, overall score, lesson exercises

done, lesson exercises difficulty level reached, and number

questions of a lesson exercises tried. These data is stored for

later usage in other models such pedagogical model.




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PEDAGOGICAL MODEL ARCHITECTURE

The pedagogical model analyze the stored information about

the student to decide the customized feedback about the

student educational level, when the student solve an exercise

correctly or incorrectly, when the student score in a lesson

exercises is bad, average or good. For example, when the

student's current score in a particular lesson exercises is less

then %50:

1. The pedagogical model display a notification message

telling the student "your performance in this lesson

exercises is bad and I suggest that you go back to lesson

and study it well then come back to the exercises and try

again",

2. The pedagogical model close the exercises screen and

take the student back to the lesson screen so the user

study the material of the lesson again

But when the student's current score in a particular lesson

exercises is between %50 and less than %70:



exercises is not good enough" and show the score in the

notification message.

2. The pedagogical model collect the questions related to

the same lesson and re-arrange then randomly and

preset them to the student to solve.

However, when the student's current score in a particular

lesson exercises is greater than %70:



exercises is good and you passed this lesson is

successfully" and the score in the notification message is

shown to the student.




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2. The pedagogical model takes the student to the next

difficulty level of the lesson to solve.

USER INTERFACE MODEL

In this student interface, student chooses the desired lesson to

learn.

Fig. 2: Intelligent Tutoring System Lesson area

In this student interface, student chooses the lesson exercises

and tries to solve as in Fig 3. After the student solves the

question, he can check to see if his answer is correct or not. If

the student cannot solve the question, he can see the solution of

the question. The student can check his current score and

overall score anytime (See Fig. 4). In case the student does not

get at least 50% as a current sore and the end of the lesson

exercises he get the notification message as seen in Fig. 5.




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Fig 3. Intelligent Tutoring System Exercises area

Fig. 4: Intelligent Tutoring System display student statistics

Fig. 5: Intelligent Tutoring System display a notification for the

student

EVALUATION

The intelligent tutoring system was presented to some

specialists in the field and was applauded by some, while others

requested to improve the system by increasing the lessons,

questions, and exercises. But on the educational material side,




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they stated that it is easy for students to understand the

lessons and follow-up at any time they want.

CONCLUSION

An Intelligent Tutoring System for teaching students computer

theory was designed and developed using the ITSB authoring

tool. We have tried to keep the design as simple as possible in

terms of explaining the material instruction, while ensuring

that the student take advantage of the questions and find

solutions for those who could not answer. An evaluation was

carried out to see the student and teacher satisfaction of the

Intelligent Tutoring System. The results of the evaluation were

satisfactory.

In the future, we will increase the lessons to cover a

greater range of educational material and increase number of

exercises of each lesson.

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adaptive intelligent tutoring system for learning computer

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